Analysis of Functional MRI for Presurgical Mapping: Reproducibility, Automated Thresholds, and Diagnostic Accuracy

Abstract

Examination of functional brain anatomy is a crucial step in the process of surgical removal of many brain tumors. Functional magnetic resonance imaging (fMRI) is a promising technology capable of mapping brain function non-invasively. To be successfully applied to presurgical mapping, there are questions of diagnostic accuracy that remain to be addressed.

One of the greatest difficulties in implementing fMRI is the need to define an activation threshold for producing functional maps. There is as of yet no consensus on the best approach to this problem, and a priori statistical approaches are generally considered insufficient because they are not specific to individual patient data. Additionally, low signal to noise and sensitivity to magnetic susceptibility effects combine to make the production of activation maps technically demanding. This contributes to a wide range of estimates of reproducibility and validity for fMRI, as the results are sensitive to changes in acquisition and processing strategies.

Test-retest fMRI imaging at the individual level, and receiver operator characteristic (ROC) analysis of the results can address both of these concerns simultaneously. In this work, it is shown that the area under the ROC curve (AUC) can be used as an indicator of reproducibility, and that this is dependent on the image thresholds used. Production of AUC profiles can thus be used to optimize the selection of individual thresholds on the basis of detecting stable activation patterns, rather than a priori significance levels.

The ROC analysis framework developed provides a powerful tool for simultaneous control of protocol reproducibility and data driven threshold selection, at the individual level. This tool can be used to guide optimal acquisition and processing strategies, and as part of a quality assurance program for implementing presurgical fMRI.